SoroushVahidi/scidocs huggingface dataset:
Closed-Loop Control of a Three-Phase Neutral-Point-Clamped Inverter Using an Optimized Virtual-Vector-Based Pulsewidth Modulation
(2008)

This paper presents a closed-loop control scheme for the three-level three-phase neutral-point-clamped dc-ac converter using the optimized nearest three virtual-space-vector pulsewidth modulation, which is a modulation that produces low output-voltage distortion with a significant reduction of the dc-link capacitance. A new specific loop modifying the modulating waveforms is proposed to rapidly control possible perturbations in the neutral-point voltage balance. An online estimation of the load displacement angle and load linear/nonlinear nature is introduced at no extra cost. The remaining part of the control is analogous to the control for a two-level converter with an appropriate interfacing to the selected modulation. The closed-loop control is designed for the case of a renewable-energy source connected to the ac mains, and its performance is analyzed through simulation and experiments.
From single cells to deep phenotypes in cancer
(2012)

In recent years, major advances in single-cell measurement systems have included the introduction of high-throughput versions of traditional flow cytometry that are now capable of measuring intracellular network activity, the emergence of isotope labels that can enable the tracking of a greater variety of cell markers and the development of super-resolution microscopy techniques that allow measurement of RNA expression in single living cells. These technologies will facilitate our capacity to catalog and bring order to the inherent diversity present in cancer cell populations. Alongside these developments, new computational approaches that mine deep data sets are facilitating the visualization of the shape of the data and enabling the extraction of meaningful outputs. These applications have the potential to reveal new insights into cancer biology at the intersections of stem cell function, tumor-initiating cells and multilineage tumor development. In the clinic, they may also prove important not only in the development of new diagnostic modalities but also in understanding how the emergence of tumor cell clones harboring different sets of mutations predispose patients to relapse or disease progression.
Sequence Covering for Efficient Host-Based Intrusion Detection
(2019)

This paper introduces a new similarity measure, the covering similarity, which we formally define for evaluating the similarity between a symbolic sequence and a set of symbolic sequences. A pairwise similarity can also be directly derived from the covering similarity to compare two symbolic sequences. An efficient implementation to compute the covering similarity is proposed which uses a suffix-tree data structure, but other implementations, based on suffix array for instance, are possible and are possibly necessary for handling very large-scale problems. We have used this similarity to isolate attack sequences from normal sequences in the scope of host-based intrusion detection. We have assessed the covering similarity on two well-known benchmarks in the field. In view of the results reported on these two datasets for the state-of-the-art methods, according to the comparative study, we have carried out based on three challenging similarity measures commonly used for string processing, or in bioinformatics, we show that the covering similarity is particularly relevant to address the detection of anomalies in sequences of system calls.
Qualitative clinical evaluation of scapular dysfunction: a reliability study.
(2002)

The purpose of this study was to determine the intrarater and interrater reliability of a clinical evaluation system for scapular dysfunction. No commonly accepted terminology presently exists for describing the abnormal dynamic scapular movement patterns that are commonly associated with shoulder injury. A method of observation was devised for clinical evaluation of scapular dysfunction. Blinded evaluators (2 physicians and 2 physical therapists) were familiarized with the evaluation method of scapular movement patterns before viewing a videotape of 26 subjects with and without scapular dysfunction. Each evaluator was asked to categorize the predominant scapular movement pattern observed during bilateral humeral scaption and abduction motions. Reliability was assessed by a kappa coefficient. Intertester reliability (kappa = 0.4) was found to be slightly lower than intratester reliability (kappa = 0.5). These results indicate that, with refinement, this qualitative evaluation method may allow clinicians to standardize the categorization of dynamic scapular dysfunction patterns.
DART: a Dataset of Arguments and their Relations on Twitter
(2016)

The problem of understanding the stream of messages exchanged on social media such as Facebook and Twitter is becoming a major challenge for automated systems. The tremendous amount of data exchanged on these platforms as well as the specific form of language adopted by social media users constitute a new challenging context for existing argument mining techniques. In this paper, we describe a resource of natural language arguments called DART (Dataset of Arguments and their Relations on Twitter) where the complete argument mining pipeline over Twitter messages is considered: (i) we identify which tweets can be considered as arguments and which cannot, and (ii) we identify what is the relation, i.e., support or attack, linking such tweets to each other.
I feel your pain: emotional closeness modulates neural responses to empathically experienced rejection.
(2011)

Empathy is generally thought of as the ability to share the emotional experiences of others. In scientific terms, this is usually operationalized as an ability to vicariously feel others' mental and emotional experiences. Supporting this account, research demonstrates that watching others experience physical pain activates similar brain regions to the actual experience of pain itself. First-hand experience of social rejection also activates this network. The current work extends these findings by examining whether the "pain" network is similarly implicated in witnessing rejection, and whether emotional closeness modulates this response. We provide evidence for each of these suppositions, demonstrating: (a) that the pain network is activated when watching a friend suffer social rejection, and (b) that interpersonal closeness with that friend modulates this response. Further, we found that the inferior frontal gyrus, critical for representing others' mental and emotional states, mediates the relationship between emotional closeness and neural responses to watching the rejection of a friend.
HPI Question Answering System in BioASQ 2016
(2016)

Question answering (QA) systems are crucial when searching for exact answers for natural language questions in the biomedical domain. Answers to many of such questions can be extracted from the 26 millions biomedical publications currently included in MEDLINE when relying on appropriate natural language processing (NLP) tools. In this work we describe our participation in the task 4b of the BioASQ challenge using two QA systems that we developed for biomedicine. Preliminary results show that our systems achieved first and second positions in the snippet retrieval sub-task and for the generation of ideal answers.
The acceptance and use of customer relationship management (CRM) systems: An empirical study of distribution service industry in Taiwan
(2011)

With the rapid change of business competitive environment, enterprise resource integration and innovative issues of business operation have gradually become the most important issues for businesses. Furthermore, many enterprises have implemented novel information technology and developing the innovative e-business applications systems such as enterprise resource planning (ERP), customer relationship management (CRM), knowledge management (KM) and supply chain management (SCM) to enhance their competitive advantages. CRM systems can help organizations to gain the potential new customers, promote the existing customers’ purchase, maintain good relationship with customers as well as to enhance the customer value, thus can improve the enterprise images. Moreover, the development and applications of CRM systems have also been considered as important issues for researchers and practitioners in recent years. For Taiwan’s industry, it has been gradually transferred from manufacturing-oriented to a service-oriented. Thus, the service industry has higher percentage in the GDP and in which the distribution service industry is the biggest one and be a core industry in the whole of service industry. The main purpose of this study is to explore the factors affecting the acceptance and use of CRM systems. Furthermore, the proposed research model was built on the basis of unified theory of acceptance and use of technology (UTAUT) and task-technology fit (TTF) framework as well as technological and managerial theories. The implications of findings for practice will be discussed. 2010 Elsevier Ltd. All rights reserved.
Spam filtering email classification (SFECM) using gain and graph mining algorithm
(2017)

This paper proposes a hybrid solution of spam email classifier using context based email classification model as main algorithm complimented by information gain calculation to increase spam classification accuracy. Proposed solution consists of three stages email pre-processing, feature extraction and email classification. Research has found that LingerIG spam filter is highly effective at separating spam emails from cluster of homogenous work emails. Also experiment result proved the accuracy of spam filtering is 100% as recorded by the team of developers at University of Sydney. The study has shown that implementing the spam filter in the context-based email classification model is feasible. Experiment of the study has confirmed that spam filtering aspect of context-based classification model can be improved.
Efficient parallel translating embedding for knowledge graphs
(2017)

Knowledge graph embedding aims to embed entities and relations of knowledge graphs into low-dimensional vector spaces. Translating embedding methods regard relations as the translation from head entities to tail entities, which achieve the state-of-the-art results among knowledge graph embedding methods. However, a major limitation of these methods is the time consuming training process, which may take several days or even weeks for large knowledge graphs, and result in great difficulty in practical applications. In this paper, we propose an efficient parallel framework for translating embedding methods, called ParTrans-X, which enables the methods to be paralleled without locks by utilizing the distinguished structures of knowledge graphs. Experiments on two datasets with three typical translating embedding methods, i.e., TransE [3], TransH [19], and a more efficient variant TransE- AdaGrad [11] validate that ParTrans-X can speed up the training process by more than an order of magnitude.
Characterizing the software process: a maturity framework
(1988)

A description is given of a software-process maturity framework that has been developed to provide the US Department of Defense with a means to characterize the capabilities of software-development organizations. This software-development process-maturity model reasonably represents the actual ways in which software-development organizations improve. It provides a framework for assessing these organizations and identifying the priority areas for immediate improvement. It also helps identify those places where advanced technology can be most valuable in improving the software-development process. The framework can be used by any software organization to assess its own capabilities and identify the most important areas for improvement.<<ETX>>
Capability Maturity Model for Software
(2001)

This paper provides an overview of the latest version of the Capability Maturity Model for Software, CMM v1.1. Based on over six years of experience with software process improvement and the contributions of hundreds of reviewers, CMM v1.1 describes the software engineering and management practices that characterize organizations as they mature their processes for developing and maintaining software. This paper stresses the need for a process maturity framework to prioritize improvement actions, describes the process maturity framework of five maturity levels and the associated structural components, and discusses future directions for the CMM.
Climbing the "Stairway to Heaven" -- A Mulitiple-Case Study Exploring Barriers in the Transition from Agile Development towards Continuous Deployment of Software
(2012)

Agile software development is well-known for its focus on close customer collaboration and customer feedback. In emphasizing flexibility, efficiency and speed, agile practices have lead to a paradigm shift in how software is developed. However, while agile practices have succeeded in involving the customer in the development cycle, there is an urgent need to learn from customer usage of software also after delivering and deployment of the software product. The concept of continuous deployment, i.e. the ability to deliver software functionality frequently to customers and subsequently, the ability to continuously learn from real-time customer usage of software, has become attractive to companies realizing the potential in having even shorter feedback loops. However, the transition towards continuous deployment involves a number of barriers. This paper presents a multiple-case study in which we explore barriers associated with the transition towards continuous deployment. Based on interviews at four different software development companies we present key barriers in this transition as well as actions that need to be taken to address these.
A Multi-User Surface Visuo-Haptic Display Using Electrostatic Friction Modulation and Capacitive-Type Position Sensing
(2016)

This paper proposes a visuo-haptic feedback system that can provide haptic feedback to multiple users on an LCD monitor using electrostatic adhesion and built-in capacitive sensors. The prototype system consists of a 40-inch LCD monitor, an ITO electrode sheet arranged on the monitor, and multiple contact pads for electrostatic adhesion. By applying low-frequency haptic voltage and high-frequency sensing voltage to each pad, the system realizes passive haptic feedback, as well as position sensing using the same components. The passive haptic feedback force exceeds 1 N at 300 V<inline-formula><tex-math notation="LaTeX">$_\mathrm{rms}$</tex-math><alternatives> <inline-graphic xlink:type="simple" xlink:href="nakamura-ieq1-2556660.gif"/></alternatives></inline-formula>. The performance of the sensing system is investigated in terms of interference, which shows the haptic voltage and existence of multiple pads can affect the sensing and can shift the output by a few millimeters. A pilot application, virtual hockey game, successfully demonstrates the sensing and haptic rendering capability of the proposed system. The effect of the interference on the demonstration is discussed.
Music, Intelligence and Artificiality
(2000)

The discipline of Music-AI is defined as that activity which seeks to program computers to perform musical tasks in an intelligent, which possibly means humanlike way. A brief historical survey of different approaches within the discipline is presented. Two particular issues arise: the explicit representation of knowledge; and symbolic and subsymbolic representation and processing. When attempting to give a precise definition of Music-AI, it is argued that all musical processes must make some reference to human behaviour, and so Music-AI is a central rather than a peripheral discipline for musical computing. However, it turns out that the goals of Music-AI as first expressed, the mimicking of human behaviour, are impossible to achieve in full, and that it is impossible, in principle, for computers to pass a musical version of the Turing test. In practice, however, computers are used for their non-human-like behaviour just as much as their human-like behaviour, so the real goal of Music-AI must be reformulated. Furthermore, it is argued that the non-holistic analysis of human behaviour which this reformulation entails is actually informative for our understanding of human behaviour. Music-AI could also be fruitfully concerned with developing musical intelligences which were explicitly not human. Music-AI is then seen to be as much a creative enterprise as a scientific one.
A 450 fs 65-nm CMOS Millimeter-Wave Time-to-Digital Converter Using Statistical Element Selection for All-Digital PLLs
(2018)

This paper presents a time-to-digital converter (TDC) that operates with a 20–64 GHz input and underpins the phase digitization function in a millimeter-wave all-digital fractional-N frequency synthesizer. A self-calibrated inductor-less frequency divider using dynamic CML latches provides an eight-phase input to a 3-bit “coarse” TDC, which is interfaced to a 5-bit “fine” TDC through a sub-sampling coarse-fine interface circuit. A wide bandwidth low dropout (LDO) on-chip regulator is used to decrease the effect of supply noise on TDC performance. A synthesized digital engine implements calibration using statistical element selection with mean adaptation to alleviate TDC nonlinearity that results from random mismatches and PVT variations. The TDC is fabricated in 65-nm CMOS along with the divider and calibration circuits, and achieves 450-fs resolution. The measured DNL and INL of the TDC are 0.65 and 1.2 LSB, respectively. The TDC consumes 11 mA from 1-V supply voltage. The TDC features a figure-of-merit of 0.167 (0.47) pJ per conversion step without (with) the frequency divider. A single-shot experiment shows that the on-chip LDO reduces the effect of TDC noise by reducing the standard deviation from 0.856 to 0.167 LSB for constant input. The prototype occupies an active area of  $502\times 110~\mu \text{m}^{\mathbf {2}}$  excluding pads.
Presupposed Content and Entailments in Natural Language Inference
(2009)

Previous work has presented an accurate natural logic model for natural language inference. Other work has demonstrated the effectiveness of computing presuppositions for solving natural language inference problems. We extend this work to create a system for correctly computing lexical presuppositions and their interactions within the natural logic framework. The combination allows our system to properly handle presupposition projection from the lexical to the sentential level while taking advantage of the accuracy and coverage of the natural logic system. To solve an inference problem, our system computes a sequence of edits from premise to hypothesis. For each edit the system computes an entailment relation and a presupposition entailment relation. The relations are then separately composed according to a syntactic tree and the semantic properties of its nodes. Presuppositions are projected based on the properties of their syntactic and semantic environment. The edits are then composed and the resulting entailment relations are combined with the presupposition relation to yield an answer to the inference problem.
Reactive power capability of the wind turbine with Doubly Fed Induction Generator
(2013)

With the increasing integration into power grids, wind power plants play an important role in the power system. Many requirements for the wind power plants have been proposed in the grid codes. According to these grid codes, wind power plants should have the ability to perform voltage control and reactive power compensation at the point of common coupling (PCC). Besides the shunt flexible alternating current transmission system (FACTS) devices such as the static var compensator (SVC) and the static synchronous compensator (STATCOM), the wind turbine itself can also provide a certain amount of reactive power compensation, depending on the wind speed and the active power control strategy. This paper analyzes the reactive power capability of Doubly Fed Induction Generator (DFIG) based wind turbine, considering the rated stator current limit, the rated rotor current limit, the rated rotor voltage limit, and the reactive power capability of the grid side convertor (GSC). The boundaries of reactive power capability of DFIG based wind turbine are derived. The result was obtained using the software MATLAB.
Mycorrhizosphere interactions to improve plant fitness and soil quality
(2002)

Arbuscular mycorrhizal fungi are key components of soil microbiota and obviously interact with other microorganisms in the rhizosphere, i.e. the zone of influence of plant roots on microbial populations and other soil constituents. Mycorrhiza formation changes several aspects of plant physiology and some nutritional and physical properties of the rhizospheric soil. These effects modify the colonization patterns of the root or mycorrhizas (mycorrhizosphere) by soil microorganisms. The rhizosphere of mycorrhizal plants, in practice a mycorrhizosphere, harbors a great array of microbial activities responsible for several key ecosystem processes. This paper summarizes the main conceptual principles and accepted statements on the microbial interactions between mycorrhizal fungi and other members of rhizosphere microbiota and discusses current developments and future trends concerning the following topics: (i) effect of soil microorganisms on mycorrhiza formation; (ii) mycorrhizosphere establishment; (iii) interactions involved in nutrient cycling and plant growth; (iv) interactions involved in the biological control of plant pathogens; and (v) interactions to improve soil quality. The main conclusion is that microbial interactions in the rhizosphere of mycorrhizal plants improve plant fitness and soil quality, critical issues for a sustainable agricultural development and ecosystem functioning.
A Hidden Markov Model for Vehicle Detection and Counting
(2015)

To reduce roadway congestion and improve traffic safety, accurate traffic metrics, such as number of vehicles travelling through lane-ways, are required. Unfortunately most existing infrastructure, such as loop-detectors and many video detectors, do not feasibly provide accurate vehicle counts. Consequently, a novel method is proposed which models vehicle motion using hidden Markov models (HMM). The proposed method represents a specified small region of the roadway as 'empty', 'vehicle entering', 'vehicle inside', and 'vehicle exiting', and then applies a modified Viterbi algorithm to the HMM sequential estimation framework to initialize and track vehicles. Vehicle observations are obtained using an Adaboost trained Haar-like feature detector. When tested on 88 hours of video, from three distinct locations, the proposed method proved to be robust to changes in lighting conditions, moving shadows, and camera motion, and consistently out-performed Multiple Target Tracking (MTT) and Virtual Detection Line(VDL) implementations. The median vehicle count error of the proposed method is lower than MTT and VDL by 28%, and 70% respectively. As future work, this algorithm will be implemented to provide the traffic industry with improved automated vehicle counting, with the intent to eventually provide real-time counts.